A Simple Stochastic Stomach Cancer Model with Application
American Journal of Theoretical and Applied Statistics
Volume 7, Issue 3, May 2018, Pages: 112-117
Received: Dec. 3, 2017; Accepted: Dec. 12, 2017; Published: Apr. 11, 2018
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Authors
Josphat Mutwiri Ikiao, Department of Mathematics, Meru University of Science and Technology, Meru, Kenya
Nyongesa Kennedy, Department of Mathematics, Masinde Murilo University of Science and Technology, Kakamega, Kenya
Robert Muriungi Gitunga, Department of Mathematics, Meru University of Science and Technology, Meru, Kenya
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Abstract
Survival analysis majors mainly on estimation of time taken before an event of interest takes place. Time taken before an event of interest takes place is a random process that takes shape overtime. Stochastic processes theory is therefore very crucial in analysis of survival data. The study employed markov chain theory in developing a simple stochastic stomach cancer model. The model is depicted with a state diagram and a stochastic matrix. The model was applied to stomach cancer data obtained from Meru Hospice. Transition probability theory was used in determining transition probabilities. The entries of the stochastic matrix T were estimated using the Aalen-Johansen estimators. The time taken for all the people under the study to transit to death was estimated using the limiting matrix.
Keywords
Stochastic Stomach Cancer Model, State Diagram, Stochastic Matrix, Transition Probabilities, Limiting Matrix
To cite this article
Josphat Mutwiri Ikiao, Nyongesa Kennedy, Robert Muriungi Gitunga, A Simple Stochastic Stomach Cancer Model with Application, American Journal of Theoretical and Applied Statistics. Vol. 7, No. 3, 2018, pp. 112-117. doi: 10.11648/j.ajtas.20180703.13
Copyright
Copyright © 2018 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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